Because of the increasingly fast processing power of modem-day computers, users have in droves been turning to computers to assist them in the examination and analysis of images of real-world data. For example, within the medical community, radiologists and other professionals who once examined x-rays hung on a light screen use computers to examine images obtained via ultrasound, computed tomography (CT), magnetic resonance (MR), ultrasonography, positron emission tomography (PET) single photon emission computed tomography (SPECT), magnetic source imaging, and other imaging techniques. Countless other imaging techniques will no doubt arise as medical imaging technology evolves.
Each of the above-identified imaging procedures generates volume images, although each relies on a different technology to do so. Thus, CT requires an x-ray source to rapidly rotate around a patient to obtain hundreds of electronically stored pictures of the patient. Conversely, for example, MR requires that radio-frequency waves be emitted to cause hydrogen atoms in the body's water to move and release energy, which is then detected and translated into an image. Because each of these techniques penetrates the body of a patient to obtain data, and because the body is three-dimensional, this data represents a three-dimensional image, or volume. In particular, CT and MR both provide three-dimensional "slices" of the body, which can later be electronically reassembled.
Computer graphics images, such as medical images, have typically, however, been modeled through the use of techniques that are inherently two dimensional in nature to some degree. One such technique is surface-rendering. Surface-rendering has its foundations in geometry-based modeling. For example, surface-rendering of a three-dimensional volume begins with a three-dimensional form of line drawing, a wireframe model, that is comprised of a network of lines and vectors. Surface-rendering replaces this network of lines and vectors with a mesh of polygons.
In the past two decades, significant advances of surface-rendering techniques have led to surface-rendered images having a great deal of realism. The polygonal model can be elaborately shaded to simulate the play of light and shadow on the object to be imaged, endowing each polygon with known or imagined surface properties. This gives the viewer the sense that he or she is looking through a window and into a virtual world.
However, surface-rendering techniques do just and only that--they render surfaces. Therefore, even with an intricately rendered and incredibly realistic surface-rendered image, there is nothing beyond the surface. The model is a hollow shell lacking the solid continuity that exists in the real world. Looking inside the shell reveals nothing.
Real-world, three-dimensional data also resists accurate imaging in accordance with geometry-based modeling techniques in other ways. Conventional geometric graphics techniques start with a simplified, extracted model of the contents of the original three-dimensional data set. The techniques must extract boundaries or surfaces from the data, and decide how to represent them with geometrical primitives (points, lines and polygons)--a process which can introduce distortions. Conventional geometric graphics techniques, therefore, assume a priori that every object within a three-dimensional domain has an already known shape or a shape which can be accurately determined.
However, three-dimensional data may not have clear boundaries that are easily represented with geometrical primitives. Thus, the user viewing such a surface-rendered imaging of the data is not viewing the data itself inasmuch as the user is viewing an interpretation of the data. Furthermore, surface-rendering requires great effort and time if presented with a complex data set, and if a faithful rendering is sought, even if the rendering is accomplished by a powerful computer.
In response to the deficiencies of geometric-based techniques such as surface-rendering, researchers have turned to three-dimensional-based volume-rendering techniques as a more accurate way to render images based on real-world data. Volume-rendering takes a conceptually simpler approach to rendering than does surface-rendering. Rather than overlay surfaces on a complex model of three-dimensional data, volume-rendering assumes that three-dimensional objects are composed of basic volumetric building blocks.
These volumetric building blocks are commonly referred to as voxels. Whereas, by contrast, the well known pixel is a picture element--i.e., a tiny two-dimensional sample of a digital image have a particular location in the plane of a picture defined by two coordinates--a voxel is a sample that exists within a three-dimensional grid, positioned at coordinates x, y, and z. The voxel has a "voxel value," as that value is obtained from real-world scientific or medical instruments. The voxel value may be measured in any of a number of different units, such as housefields, which are well known to those of ordinary skill within the art. For a given voxel value, a transparency value, to indicate its relative opacity vis-a-vis other voxels, as well as a color value, to indicate its color, may also be assigned (for example, in a particular tabling including such mappings).
Using volume-rendering, any three-dimensional volume can be simply divided up into a set of three-dimensional samples, or voxels. Thus, a volume containing an object of interest is dividable into small cubes, each of which contain some piece of the original object. This continuous volume representation is transformable into discrete elements by assigning to each cube a voxel value that characterizes some quality of the object as contained in that cube.
The object is thus summarized by a set of point samples, such that each voxel is associated with a single digitized point in the data set. As compared to mapping boundaries in the case of geometric-based surface-rendering, reconstructing a volume using volume-rendering requires much less effort and is more intuitively and conceptually clear. The original object is reconstructed by the stacking of voxels together in order, so that they accurately represent the original volume.
Although more simple on a conceptual level, and more accurate in providing an image of the data, volume-rendering is nevertheless still complex. A key requisite of volume rendering is the use of the entire voxel data set to create an image. In one method of voxel rendering, called image ordering or ray casting, the volume is positioned behind the picture plane, and a ray is projected perpendicularly from each pixel in the picture plane through the volume behind the pixel. As each ray penetrates the volume, it accumulates the properties of the voxels it passes through and adds them to the corresponding pixel. The properties accumulate more quickly or more slowly depending on the transparency of the voxels.
In another method, called object-order (or compositing or splatting), the voxel values are also combined to produce image pixels for display on a computer screen. The image plane is positioned behind the volume, and each pixel is assigned an initial background value. A ray is projected perpendicularly from the image plane through the volume to the viewer. As the ray encounters each successive layer of voxels, the voxel values are blended into the background, forming the image according to each voxel's interpreted opacity. The image rendered in this method as well depends on the transparency of the voxels.
Due to such variables present in the volume-rendering process, such as transparency as has been described, volume-rendering does not by itself ensure that the resulting image of data is visually realistic or is the image desired by the end user. The volume-rendering must be conducted correctly to ensure that the image is generated accurately. Moreover, different uses of the resulting image are such that the volume-rendering be performed differently from one use to another. For example, the volume-rendering of cardia tissue requires different opacity presets than does the volume-rendering of bone mass.
Furthermore, even within respect to the same use, volume-rendering may be required to be performed differently depending on the application of that use. For example, one physician may be interested in the most dense cardia tissue of a data set, while another physician may be interested in the least dense cardia tissue of the data set. In either case, the volume-rendering is conducted differently to accentuate the desired features of the data. Typically, color is also added to emphasize the desired features.
Unfortunately, however, the end users who can most benefit from the advantages of volume-rendering are not typically volume-rendering computer graphics experts. With respect to images rendered from sets of medical data (such as patient studies), the end user who can most benefit from volume-rendering techniques are physicians, such as radiologists, and technicians. Volume-rendering enables such users to have access to medical images that may display indicia of disease and medical problems otherwise unavailable to these doctors and technicians.
A physician, however, cannot be expected to master the subtleties of volume-rendering as a computer graphics expert may be expected to. Thus, providing physicians with a volume-rendering tool is ineffective if that tool is not easy to use, and does not permit the physician to quickly conduct a volume-rendering of an image of medical data with the correct presets and in the correct manner. Only in this way is volume-rendering of any use to the physician. That is, only if a physician, or other non-expert end user, can easily and quickly conduct a volume-rendering can it be expected that the physician or other non-expert end user will generate a rendered image that is capable of assisting the physician in making a more informed analysis, such as a medical diagnosis.